Learning the therapeutic use of drugs from the three-dimensional spatial information of their molecular structure with convolutional neural networks
Abstract The development of new molecules is a multi-stage process and clinical trials to verify their efficacy cost billions of dollars each year. Machine learning is a tool that is rapidly advancing in image, voice, and text recognition, and working in silico would increase the ability to predict...
| Autores: | , |
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| Formato: | artículo |
| Estado: | Versión borrador |
| Fecha de publicación: | 2021 |
| País: | Colombia |
| Recursos: | Universidad Tecnológica de Bolívar |
| Repositorio: | Repositorio Institucional UTB |
| Idioma: | español |
| OAI Identifier: | oai:repositorio.utb.edu.co:20.500.12585/12367 |
| Acesso em linha: | https://hdl.handle.net/20.500.12585/12367 |
| Access Level: | acceso abierto |
| Palavra-chave: | Chemoinformatics; Drug Discovery; Topographic Mapping LEMB |
| Resumo: | Abstract The development of new molecules is a multi-stage process and clinical trials to verify their efficacy cost billions of dollars each year. Machine learning is a tool that is rapidly advancing in image, voice, and text recognition, and working in silico would increase the ability to predict and prioritize a drug's function. In this research we asked whether the function of therapeutic drugs can be predicted from the stereochemical configuration of the molecule. We use convolutional neural networks to predict the therapeutic use of drugs, trained with both two-dimensional and three-dimensional information of their chemical structure. The model trained with only six views of the 3D information of the molecular structure improved the accuracy by 10 over the model trained with the 2D information. © 2021, Universidad Nacional de Colombia. All rights reserved. |
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